The importance of vector abundance and seasonality

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TECHNICAL REPORT

The importance of

vector abundance and

seasonality

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ECDC TECHNICAL REPORT

The importance of vector abundance and seasonality

Results from an expert consultation

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This report by the European Centre for Disease Prevention and Control (ECDC) and the European Food Safety Authority (EFSA) was coordinated by Céline Gossner, and written and produced by the following contributors:

G.R.W Wint (ERGO, Oxford), B. Alten (Hacettepe University, Beytepe-Ankara, Turkey), T. Balenghien (CIRAD, Montpellier, France), E. Berriauta (University of Murcia, Spain), M. Braks (RIVM, Netherlands), J Medlock (Public Health England, Salisbury, UK), D. Petric (University of Novi Sad, Novi Sad, Serbia), F. Schaffner (Francis Schaffner Consultancy, Riehen, Switzerland), S. Dhollander (EFSA, Parma, Italy), C.M. Gossner (ECDC, Stockholm, Sweden), O.J.T. Briët (ECDC, Stockholm, Sweden), E. Ducheyne (Avia-GIS, Zoersel, Belgium)

This report was a deliverable in a contract that was awarded by ECDC to VectorNet; contract title: Specific Contract No. 4 – ECD. 7499 in the framework of VectorNet project 2014–2018; contract number: OC/EFSA/AHAW/2013/02- FWC1; report on the relevance of seasonality and abundance of vectors.

Acknowledgements

The authors are grateful to the entire VectorNet community for working hard during four years to collect the data and for sharing the knowledge upon which this document is based. We are also grateful for the support of the team at Avia-GIS for providing the organisation and perspective that underpinned the whole VectorNet project. The authors drew on the expertise of many colleagues in the VectorNet project, including Agustin Estrada Pena, Zati Vatansever, Thomas Jaenson, Zdenek Hubalek, Kayleigh Hansford, Alexander Vaux, Petr Volf, Vladimir Ivovic, Vit Dvorak, René Bødker, Simon Carpenter, Isabel Pereira da Fonseca, Marta Verdun Castello, Vincent Robert and Helge Kampen.Thanks are also due to Céline M. Gossner at ECDC and Sofie Dhollander at EFSA for their guidance and encouragement.

Suggested citation: European Centre for Disease Prevention and Control and European Food Safety Authority. The importance of vector abundance and seasonality – Results from an expert consultation. Stockholm and Parma:

ECDC and EFSA; 2018.

Stockholm and Parma, November 2018 ISBN 978-92-9498-271-1

DOI 10.2900/37171

Catalogue number TQ-06-18-175-EN-N

Cover photo: USDA, Scott Bauer, Creative Commons attribution non-commercial (CC BY 2.0) license via Flickr

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Figures

Figure 1. Locations with abundance values derived from standardised sampling of active vectors, VectorNet database, as of March 2018 ... 10

Figure 2. Comparison of values extracted from spatially predicted surfaces of maximum abundance with ranked normalised abundance category for Culicoides imicola (top) and probability of presence of C. dewulfi (bottom) .... 17

Tables

Table 1A. Summary of expert opinion of vector-related drivers of epidemiological concepts ... 6

Table 1B. Summary of expert opinion of vector-related drivers of epidemiological concepts ... 7

Table 2. Sum of normalised scores by epidemiological concept ... 9

Table 3. Sum of normalised scores by epidemiological concept and vector group ... 9

Table A1. Expert opinion of vector-related drivers of epidemiological concepts: ticks ... 26

Table A1, continued ... 28

Table A2. Expert opinion of vector-related drivers of epidemiological concepts: midges ... 29

Table A3. Expert opinion of vector-related drivers of epidemiological concepts: mosquitoes ... 35

Table A4. Expert opinion of vector-related drivers of epidemiological concepts: sandflies ... 37

Table A5. Expert scores of importance of drivers of epidemiological concepts ... 41

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Abbreviations

AHSV African horse sickness virus

BTV Bluetongue virus

CCHFV Crimean-Congo haemorrhagic fever virus

CO2 Carbon dioxide

ECDC European Centre for Disease Prevention and Control

EEA European Economic Area

EFSA European Food Safety Authority

EID2 Enhanced Emerging Infectious Disease database

ESA European Space Agency

E–W East to west

GBIF Global Biodiversity Information Facility

R0 Basic reproduction number

S–N South to north

TBD Tick-borne disease

TBEV Tick-borne encephalitis virus

WNV West Nile virus

WNF West Nile fever

Glossary

Abundance Quantity (i.e. a number of specimens of a species in a site at a given time), which can be expressed in absolute terms, relative terms, as an index and as a rank or category.

Basic reproduction number The number of secondary cases per case in a naïve population [1].

Human activity Anthropogenic factors, which affect potential contact rate with vectors, as well as vector spread, and which may include farming practices, human behaviour and moving of animals.

Human biting rate The number of bites received per day [2].

Intrinsic incubation period The time taken by an organism to complete its development in the definitive host.

Longevity A statistical measure of the average time an organism is expected to live [3].

Pathogen transmission by vectors The process of a pathogen being passed from vector to host and vice versa.

Seasonality The change in abundance of a species over the course of a calendar year. It is defined as the fluctuation of the active population over the period that it is present.

Vector-free period Period of year when active (adult) vectors are not present.

Vector season The period between the date from which active individuals are first recorded and the date after which it is no longer recorded (which is the end date of the season). It is also referred to as season length.

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Executive summary

Knowing where vector species occur is crucial for the assessment of vector-borne disease risk. This was recognised by EFSA and ECDC, who, over the period 2014–2018, funded VectorNet, a European network for gathering and sharing data on the geographic distribution of arthropod vectors of disease agents affecting humans and livestock.

The VectorNet database contains distribution data on four vector groups: mosquitoes, sandflies, Culicoides midges and ticks. The majority of these data are on vector presence or absence, but since 2015, count data from a number of targeted field surveys, which used standardised sampling methods and that were funded through the network, have been added.

The objective of this document is to describe an assessment, for each vector group, of whether vector count data (abundance) and the way these change throughout the year (seasonality) can provide useful information about epidemiological processes of interest, and therefore, whether resources should be devoted to collecting such data.

The document also summarises what measures of abundance and seasonality can be collected for each group, what gaps remain in the sampling coverage and what can be done to fill these gaps. Furthermore, it provides guidance for prioritising the acquisition of information.

For each vector group, expert opinion was canvassed to provide a semi-quantitative assessment of whether and how vector abundance and seasonality, each individually or in combination, are related to pathogen establishment, persistence, transmission and spread, are predictor variables useful for early warning, and are useful to spatially and temporally target vector or pathogen control.

Abundance and seasonality may each be closely related to pathogen establishment, persistence, transmission and spread, and thus each serve as epidemiologically relevant indicators. However, their significance may only become apparent when combined with each other or with other variables that are related to the vector’s ability to transmit infection — for example, the proportion of vectors that are infected with the pathogen (the vector infection rate).

The importance of abundance and seasonality was, therefore, considered for each in isolation and for both combined as well as both combined with other variables that may determine vector occurrence such as vector behaviour, vector infection rate and habitat.

The information needed to make the assessments was collated from dedicated VectorNet group discussions, field sampling studies, and both peer-reviewed and grey literature. Each vector group leader scored the importance of abundance and seasonality (alongside other vector-related characteristics) for the assessment of whether early warning of pathogen introduction must be issued, for the assessment of pathogen establishment and spread and to guide vector control strategies.

In the group discussions, experts assessed that abundance, by itself, plays a role in the epidemiological processes of most vector-borne infections, although the importance of the role varied among the vector groups. Seasonality was assessed to have relatively little influence on vector abundance but to be more important in determining the phenology of overwintering vector populations.

In all vector groups, abundance was considered to have an effect on pathogen establishment and transmission.

This applied less to early warning systems and assessments of pathogen persistence. Abundance measures are routinely used for targeted control of mosquitoes, but this is not the case for the other vector groups.

For ticks, a special case exists: tick presence was considered to be a better indicator of risk of pathogen

transmission than tick abundance if abundance is considered alone. Indeed, the number of ticks observed by itself cannot be considered an indicator of risk of infection. This changes when abundance is combined with vector infection rates. If information on abundance and seasonality is combined with other vector-related characteristics (such as vector infection rate), then abundance and seasonality are judged to be important.

This argues for acquiring both abundance and seasonality data on the disease vectors of public and animal health concern, alongside collecting and recording additional variables such as vector infection rates and biting rates.

While the VectorNet database already contains a substantial amount of vector abundance and seasonality data collected during targeted field surveys, important gaps at the continental scale remain. Additional data are, therefore, needed to fill these gaps, for example through additional field work and literature searches. Additionally, mechanistic, environmental and spatial distribution modelling may aid to maximise the use of the data currently available as well as help minimise the amount of additional information that would be needed to generate large- scale transmission risk maps. This report outlines the sampling strategies and prioritisation steps needed to acquire such information most efficiently.

Finally, in order to understand and ultimately reduce the potential risk of infection by (new) vector-borne pathogens, it is useful to compile comprehensive abundance and seasonality databases. Such a reduction of the potential risk of infection will, however, only be possible if there is the political will to support long-term

transboundary programmes and if the necessary professional skills base is maintained.

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Background

Knowing where a vector species occurs is crucial information for vector-borne disease risk assessment. This was recognised by EFSA and ECDC who funded VectorNet, the European Network for gathering and sharing data on the geographic distribution of arthropod vectors transmitting both human and animal disease agents (2014–2018). The maps generated under VectorNet are very widely used by the professional community and include published vector data, field data collected under targeted VectorNet field surveys, and information provided on a voluntary basis by the extensive expert network.

The VectorNet data database currently includes mainly presence/absence data on four vector groups: mosquitoes, sandflies, Culicoides midges, and ticks. Presence/absence data may be used as a first indication of potential risk for vector-borne pathogen transmission. The presence of a vector alone is, however, unlikely to be a reliable indicator of pathogen transmission, and it is reasonable to assume that measures of the number of vectors, also called abundance, would provide better indication of transmission risk.

In temperate regions, such as Europe, the importance of abundance and seasonality as factors in arthropod-borne (arbo) viral outbreaks vary widely from region to region [4]. In general, active vectors are less numerous or even absent during the winter time, whereas they are more numerous during the rest of the year and will show one or two peaks in abundance, usually in summer time. The timing and pattern of these abundance cycles, called seasonality, affects the impact of vectors on infection dynamics. In turn, the variation in vector numbers and in vector activity are related to environmental drivers such as temperature and humidity. Neither vector abundance nor seasonality can be measured by taking a single sample at a given time and place, and sequential samples during longitudinal studies are required to obtain information on seasonality.

A number of epidemiological indicators are relevant to the preparation of outbreak response and mitigation strategies and might be used for early warning of pathogen introduction, and as indicators of pathogen

establishment, persistence, transmission and spread. These indicators are determined by a range of factors related to the vector, its hosts, the geographical location, and the characteristics of the pathogens themselves. Vector abundance and its associated seasonality are, therefore, only two of the epidemiological drivers, and the degree to which they are relevant varies between the vector groups and between the pathogens these vectors carry.

The objective of this document is to assess the degree to which estimates of vector abundance and seasonality, for each vector group, can be used to inform assessments of epidemiological concepts. It does not aim to be a comprehensive overview of the subject but rather to inform decision makers whether it is worth investing the resources needed to acquire abundance and seasonality information. The document also summarises what measures of abundance or seasonality can be collected for each group, and the extent to which these are available within the VectorNet database. The document also outlines what gaps remain and what can be done to fill them.

Types of measures of abundance and seasonality

Abundance. According to Eldridge and Edman [5], abundance is a general term that addresses the question:

‘How many?’. In this report, the term abundance refers to a quantity (i.e. a number of specimens of a species in a site at a given time), and can be expressed in absolute terms, relative terms, as an index and as a rank or category. Density, expressed as the number of individuals per unit area or volume at a given time point, is one measure of abundance.

Absolute abundance. The number of specimens per unit area or volume is called the absolute population [6].

When it is the number of a species per unit of the habitat, e.g. per volume of water or per host, it is called

‘population intensity’ [6].

Relative abundance. The number of specimens collected in a specific trap or sampling method, which is a sample of the population. When vectors are sampled by traps containing an attractant (CO2, light, odour, sound, animal or human bait) or by dragging or sticky papers, their relative abundance is expressed as numbers per standardised sample. Only when collection methods are standardised, it is possible to compare relative abundance estimates between different places and/or different times.

Index. Individual specimens are not necessarily counted, but their occurrence in a breeding site or habitat is recorded. This measure is expressed as the number of sampled sites with vector presence divided by the total number of sampled sites. The resulting estimate is the abundance index. Examples are the container index and the house index for yellow fever mosquito (Aedes aegypti) surveillance [7,8].

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Rank/category. Sometimes, relative densities across a large study area (e.g. Europe) are collected and classified into categories (e.g. high, medium, low), when the collection methods are not standardised. This provides an indication of the rank of the abundance on such a large scale. This can only be achieved when the category limits are defined by taking the entire area into consideration. Categorical density estimates can be useful as input to risk assessment models such as the Rift Valley fever risk assessment for EFSA [8].

Seasonality. The change in abundance of a species over the course of a calendar year is called the seasonality, which is defined as the fluctuation of the active population over the period during which it is present. The season and seasonality can vary from year to year and is influenced by a variety of landscape-level drivers including climate, vegetation, and host availability. Especially for long-lived vectors such as most tick species, fluctuations in the number of active individuals may differ from fluctuations in the population as a whole.

The more practical term ‘vector season’ or ‘season length’ is defined as the period between the date from which active individuals are first recorded (which is the start date of the season) and the date after which they are no longer recorded (which is the end date of the season). The ‘vector-free’ period is then defined as a period when active (adult) vectors are not present.

Using abundance and seasonality data

Vector abundance and seasonality data may be used to inform a number of epidemiological concepts that are of particular public and veterinary health interest. These include the early warning of pathogen introduction, the potential for pathogen transmission, its establishment and persistence, the consequent spread of the pathogen to new localities and the control of pathogen spread (these are defined in the Methods section). Many factors may influence these concepts, including factors related to vector ecology and behaviour, and they interact in a non- linear way, as recognised by Ross in 1916 when he published the first mathematical vector-borne disease model for malaria infection [9].

The interaction of these factors (parameters) is also illustrated by the estimation of, for example, the basic reproduction number (R0), which can be considered to be a measure of the likelihood of success of the pathogen’s establishment [1]. For example, the R0 of malaria can be calculated in a formula using as parameters the number of bites per human per day (human biting rate or aggressive density, ma), the number of bites per day by each female vector (feeding rate, a), the longevity of vectors, the length of the intrinsic incubation period and of the duration of pathogen presence in blood [10]. Under the model assumption of homogeneous mixing (each human has the same probability to be bitten by each vector), the aggressive density can be calculated as ma = [(number of vectors/number of humans) x biting rate], and vector numbers are therefore an integral part of this formula and linearly related to R0.

R0 can serve as an estimate of the level of (potential) pathogen circulation between vector and host.

In vector-borne disease systems in which the pathogen reservoir species is also the host of interest to public or veterinary health, R0 and the risk of infection (in hosts of interest) are similar, correlating linearly with vector abundance. The linear relationship may not hold if, for example, the hosts in which infection is of interest are

‘dead-end hosts’ – i.e. hosts from which the pathogen cannot be transmitted onwards by the vector. An example is West Nile fever. A high transmission rate of West Nile virus (WNV) among birds by mosquitoes does not directly imply that horses or humans will become infected and sick. Horses and humans are dead-end hosts that do not contribute to the virus transmission cycle, and therefore are not part of ‘classical’ R0 calculations. Another example is Lyme borreliosis. The causative agent of this disease (Borrelia burgdorferi) cycles between ticks and wildlife hosts such as small mammals and birds, while humans are dead-end hosts. Therefore, high transmission rates between ticks and wildlife do not necessarily imply a high risk of transmission between ticks and humans.

Methods

Abundance and seasonality cannot be considered in isolation from other vector characteristics and factors that influence the ability of vectors to carry and transmit pathogens. At the start of VectorNet, four experts were appointed as vector group leaders, each responsible for the management of data for one of the following vector groups: mosquitoes (Francis Schaffner), ticks (Jolyon Medlock), sandflies (Bulent Alten) and Culicoides biting midges (Thomas Balenghien). In order to assess the importance of abundance and seasonality in a wide context, these vector group leaders were asked to assess the importance of a range of vector characteristics to a series of epidemiological concepts. The group leaders were also asked to assess whether quantitative estimates of each vector characteristic are related to specific epidemiological parameters.

The approaches used to make the assessments were developed by consensus during a face-to-face meeting of the vector group leaders and members of the VectorNet consortium. This meeting was followed by a number of

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Vector-related parameters

 Vector presence/absence. Whether a population of a species is present or not at any time of year. Vector presence data can be collected by ad hoc as well as systematic sampling efforts. Confirmation of absence requires a more intensive and standardised sampling regime.

 Vector abundance. Any measure of the number of vectors per sample, collected in a known and

standardised way [11], at some point during a year. The vector may be sampled as epidemiologically active stages or inactive stages (e.g. mosquito eggs and larvae).

 Vector seasonality. Timing and duration of the period of vector activity or presence during the year, which embraces the start and end dates of the vector season and, by inference, the period when vectors are not present (the ‘vector-free period’).

 Vector infection rate. Proportion of vectors in which the pathogen has been detected (out of the total tested).

 Vector behavioural traits. Behavioural traits that may be related to a vector’s host seeking and blood meal feeding efficiency. These include endo/exophagy, circadian activity, flight capacity, biting rate, longevity, etc. Many of these factors are incorporated into epidemiological models that quantify the risk of

establishment and transmission of the pathogen.

 Reservoir host numbers. Any standardised indicator of numbers of one of more host species.

 Human activity. Anthropogenic factors, which affect potential contact rate with vectors, as well as vector spread, and which may include farming practices, human behaviour and moving of animals.

 Habitat change. Environmental changes affecting vector abundance or distributions.

Epidemiological concepts

 Early warning. Systems designed to provide advance warning of pathogen introduction or to detect pathogen circulation before the onset of disease in hosts of interest (on time to allow for implementation of prevention methods).

 Pathogen transmission by vectors. The process of a pathogen being passed from vector to host and vice versa.

 Pathogen establishment. A pathogen is considered to be established if there is at least one confirmed autochthonous case of transmission.

 Pathogen persistence. A pathogen is persistent if transmission in a given place continues from one transmission season to the next (by, for example virus overwintering).

 Pathogen spread. The movement of the pathogen to a previously pathogen free area. This includes, but is not limited to, vector-mediated spread.

 Pathogen/disease control. Any measures implemented to reduce levels of pathogen transmission, or disease prevalence. This includes control of the vector and of the disease in hosts.

As this document aims to provide an overview of the importance of abundance and seasonality of several groups of vectors of multiple diseases in relation to these epidemiological concepts, a scoring system, based on the VectorNet expert opinions and complemented with literature information, was applied. This scoring system, despite its limitations, was selected as the most appropriate method to achieve the objectives over more formal expert elicitation approaches such as Delphi elicitation. In addition to the expert opinion and data collected from peer- reviewed and grey literature, VectorNet experts also used material derived from a series of dedicated discussions among vector group network members at VectorNet annual general meetings and selected evidence from VectorNet field sampling studies.

The ‘importance scores’ assigned to each vector characteristic ranged from 0 to 4 (0: do not know, 1: no

importance, 2: importance unlikely, 3: probably important, 4: certainly/almost certainly important). Each score was accompanied by a supporting comment which represented expert opinion unless formal references were supplied.

A table was completed by each vector group leader (see Annexes).

To enable comparison between the vector groups and across the epidemiological concepts, the original scores from Tables in the Annexes were normalised so that the sum of scores for each vector group is 100. To provide some indication of the interaction between the various factors considered, these normalised scores for each vector characteristic were also summed for each epidemiological concept and each vector group.

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Results and discussion

Assessment of the importance of abundance and seasonality

The group leaders’ expert opinions and scoring are presented in Tables A1–A4, and the scoring is summarised in Table A5 in the Appendix. These tables have been condensed into two overview tables that summarise the results and highlight the similarities and contrasts between the different groups. Tables 1A and 1B provides a textual overview of the comments from each vector group, while Tables 2 and 3 give the normalised scores to help compare the assigned importance levels by epidemiological concept and vector group.

This series of tables suggests quite clearly that both abundance and seasonality (alone or in combination with other factors) are important for the majority of the epidemiological concepts for all vector groups and therefore should be included in any quantitative assessments of infection risk. This is not unexpected, particularly for abundance, given its central place in epidemiological models such as the one employed to calculate the reproductive number (R0), outlined above.

There is thus a good case for acquiring quantitative data on these two parameters, and the data gathered can be used to provide useful quantitative estimates for a range of epidemiological concepts. The results also show, however, that the interpretation of abundance and seasonality data is not straightforward, as their contribution to infection dynamics is complex and often contradictory.

For ticks, vector numbers are unlikely to inform early warning. More tick vectors may, however, mean more chance of pathogen establishment in reservoir hosts and an increased likelihood of pathogen persistence. Tick abundance is weakly related to pathogen transmission as vector infection rates can be very variable and may compensate for low numbers. These interactions are illustrated by the example of rural and urban ticks and Lyme borreliosis provided in Box 1A. Pathogen spread is mediated largely by the movement of hosts and is more dependent on synchrony between vector and host than on vector abundance. Control tends to be focused on the host and is not dependent on vector numbers.

For mosquitoes, high abundance is likely to be linked to increased likelihood of pathogen establishment, and an increase in abundance may sometimes provide an early warning of potential pathogen establishment and transmission. However, this relationship is not necessarily linear, and high vector abundance does not necessarily mean that there is a high risk of pathogen transmission (see Box 1B), or that either vector or pathogen will spread.

Control may be best targeted to areas of high mosquito abundance where the aim is to reduce vector numbers.

Because sandfly-borne disease and vector distributions usually match, a rise in abundance might provide some early warning of pathogen introduction, though there is too little direct evidence to consistently link sandfly numbers to increased pathogen establishment or infection risk (Box 1C). Vector abundance tends to be linked more closely to vector infection levels in outbreak situations than in situations where sandfly-borne diseases are

considered to be endemic. The spread of sandfly-borne pathogens may be faster when populations are abundant, like with all vector-borne pathogens, but spread may be constrained by the heterogeneity of the local microhabitats (there might be barriers of unsuitable habitats that the vector cannot cross). Sandfly control is normally targeted to areas, and at times, where confirmed disease case numbers are high; it is usually not guided by vectors

abundance.

For midges, populations rising above an abundance threshold may provide an early warning, and such events have been theoretically implicated in increased establishment of midge-borne pathogens like bluetongue (Box 1D). As is the case for other groups, abundance is a component of the basic reproductive number and vectorial capacity, which include transmission rates. Local spread of pathogens to surrounding areas may be high from farms with high midge abundance, and, as for ticks, control is most often focused on the pathogen within the host rather than the vector.

For all vector groups, seasonally unfavourable conditions in the environment may prevent activity or survival of vector life stages that are capable of transmission throughout the year. This affects pathogen persistence unless the pathogen survives the ‘vector-free period’ in the mammalian host or is transmitted vertically across vector life stages. Climatic seasonal changes are also closely linked to vector activity and development rate which will have some impact on vector numbers and pathogen transmission.

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Table 1A. Summary of expert opinion of vector-related drivers of epidemiological concepts

Concept* Early warning of pathogen introduction Pathogen establishment Pathogen persistence

Vector presence Vector presence may not mean the pathogen is also present. For ticks, presence may not imply risk of infection, as ticks may not be established. Mosquitoes and midges may be present and established but not infected.

Sandfly and disease distributions usually match.

For tick-borne pathogens, especially zoonotic ones, vectors are usually needed for transmission to hosts. Many midge- borne pathogens have multiple vectors, so the absence of one vector species does not prevent establishment of pathogens.

For mosquitoes, presence is a prerequisite of establishment but this is highly influenced by other factors such as climate or host numbers. Sandfly-borne pathogens are found where there are vectors.

For all groups, pathogen persistence requires the presence of the vector. If a pathogen has multiple vectors, the link between persistence and a single vector may be less close. If hosts help to substantially amplify the pathogen (as for some tick-borne pathogens), persistence may be determined by host rather than vector.

Vector

abundance For less abundant vectors like ticks, vector infection rates are likely to be a better indicator of the risk of pathogen introduction than numbers of ticks. For more abundant species like midges and mosquitoes, populations rising above a threshold density may provide warning of potential transmission.

More vectors may mean more chance of establishment of tick-, mosquito- and midge-borne pathogens. There is little field evidence to link high sandfly numbers with increased pathogen establishment.

For many tick-borne pathogens, both vector and host abundance need to be high to lead to persistence. For all vector groups, pathogen persistence is likely to be linked to the vector’s capability of surviving throughout the year as infected nymphs, larvae or adults, rather than survival of the pathogen in the host.

Vector

seasonality The start of seasonal increase of midge, sandfly and mosquito vector populations or feeding activity in ticks could provide some short-term warning of pathogen transmission.

Tick development may take several years, and longer seasons with suitable climate may encourage pathogen development.

Pathogens of other vectors are more likely to establish themselves if there is no vector free period.

The longer the season, the more likely that vectors will become infected, be able to transmit pathogens to humans or reservoir hosts and will be able to overwinter to ensure pathogen persistence.

Vector infection

rate High vector infection rates are likely to provide early warning of pathogen introduction into uninfected host populations for all vector groups. For midges, vector infection rates are, however, generally too low for this to be a reliable early warning indicator.

High vector infection rates in all vector groups will increase likelihood of pathogen establishment, especially if there are overwintering infected vectors.

Vector infection rates are a good general indicator of the likelihood of pathogen persistence, where vectors are abundant, though less so for midges because of their low infection rates.

Vector behavioural traits

If accompanied by high vector abundance and vector infection rates, high biting rates provide warning of disease. Biting rates are unlikely to be a practical indicator for midges, as infection rates are often very low.

For ticks, only co-feeding by several stages is likely to increase establishment. For the other groups, biting rate and longevity combined with abundance may help identify where pathogen establishment is more likely.

For ticks, host rather than vector density or activity is the more important factor. For mosquitoes and sandflies, high biting rates combined with high abundance will lead to increased persistence.

Reservoir host

numbers Tick- and mosquito-, but not midge-borne pathogens are more likely to become introduced in areas with abundant hosts.

Sudden increases in densities of hosts of sandflies may be followed by epidemics.

Tick- and mosquito-borne pathogens may be more likely to become established in areas supporting high (amplification) host densities. Rises in host densities may be followed by epidemics (sandflies).

Host density is linked to pathogen persistence in all vector groups, though only if it exceeds a threshold and particularly if the pathogen overwinters in the host.

Human activity If human activity (e.g. trade, livestock farming and movement, tourism) determines contact rates with infected vectors, the occurrence of such activity might be used to identify where new infections are more likely.

Human activity may aim to prevent establishment and may create conditions such as concentrations of hosts, or vector overwintering sites where establishment is more likely.

Human activity may aim to prevent persistence, but it may also create conditions such as concentrations of hosts, or vector overwintering sites where persistence is more likely.

Habitat change Habitat or environmental change can create areas that are suitable for vectors and are therefore known before the vectors become established. The reverse is also true. This is less important for mosquitoes and midges.

Habitat or environmental change can create new areas suitable for vectors and allow them to become established.

The reverse is also true. This is least important for midges as they are less associated with specific natural habitats than other vectors.

Habitat or environmental change can create new areas that are suitable for vectors and allow them to persist. The reverse is also true.

This is least important for midges.

*: for definitions, see Methods section.

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Table 1B. Summary of expert opinion of vector-related drivers of epidemiological concepts

Pathogen transmission to hosts Pathogen and vector spread Pathogen transmission control (vector control and other) Vector presence Vector presence is not directly linked to pathogen

transmission, especially if vectors are widespread. Vector role in transmission is reduced if there are other transmission routes (such as nosocomial) or via milk (e.g.

tick-borne encephalitis).

Vector presence is not directly linked to pathogen spread, especially if the vectors are widespread. Vector presence in periendemic regions may not imply spread for sandfly-borne pathogens. As the vectors are limited by microhabitat conditions with very patchy suitability, this prevents spread from patch to another.

Control is by definition only needed where the vectors are present, but may not be needed everywhere they are present, especially if the vectors are not restricted to particular habitats or very abundant like midges and mosquitoes.

Vector

abundance For midges, low abundance restricts pathogen transmission, and vector abundance is a component of the basic reproductive number and vectorial capacity calculated for midges and mosquitoes. Tick abundance is less closely related to pathogen transmission and vector infection rates can be very variable and compensate for low numbers. For all vectors, however, pathogen transmission and disease occur when vectors are abundant and when they are not.

Vector abundance may enhance local spread, for example from farms with high midge abundance. Long-distance spread may be more affected by other factors like bird migration routes (ticks), wind and climate (midges), trade or animal movements (mosquitoes, midges). Sandfly spread is constrained by the patchiness of the local microhabitats.

Control may be best targeted on areas of high vector abundance especially of mosquitoes where the aim is to reduce vector numbers. For ticks and midges, the control is often focused on the hosts and may not primarily be intended to reduce vector numbers in the environment as a whole, and for sandfly-borne diseases the control is targeted on disease case numbers not vector abundance as most such diseases are asymptomatic.

Vector

seasonality The longer the season, the more likely that vectors will become infected, be able to transmit pathogens to humans or reservoir hosts and will be able to survive over the winter to ensure pathogen persistence.

The longer the season, the more chance for spread, provided the neighbouring habitat is suitable (for sandflies) and host and vector activity coincide (for ticks).

Vector-free periods determine when restrictions to animal movement are relaxed and vaccination campaigns can be run. Long seasons mean control programmes need to run for longer (sandflies and mosquitoes). Abundance cycles affect timing of public health measures against tick-borne diseases.

Vector infection

rate High infection rate is likely to increase pathogen transmission and disease risk. However, for very abundant groups like midges this relationship is not linear, so the impact of low infection rates can be offset by high abundance.

High vector infection rates will promote pathogen spread in all vector groups, but this may be counteracted by limited host numbers for ticks, and habitat unsuitability in sandflies.

Vector infection rate is a useful indicator for target control, providing sufficient sampling is practical.

Vector behavioural traits

For ticks, transmission is highest when human and vector activity are highest. High biting rate and high abundance of the other groups will increase transmission.

Ticks will spread fastest when high host density and vector activity coincide. For other vectors, high biting rate, longevity and abundance will promote spread.

Tick control is more effective if vectors are active. Biting rate of other vectors may help prioritise areas for control and be used as an indicator to assess impact.

Reservoir host

numbers The vectors rather than hosts drive the transmission of tick borne disease. However, high host density is associated with increased transmission rates of tick, midge and sandfly-borne pathogens.

Movement of infected hosts spreads pathogens. High host densities mean faster spread rates (midges).

Control of tick vectors on the hosts may reduce pathogen, but not if infection persists in the host. For the other vector groups control targeting is unlikely to be determined by host numbers.

Human activity If human activity (trade, livestock farming and movement, tourism) determines contact rate with infected vectors, then it may promote transmission. Other human activities like vaccination, vector control or host treatment are meant to break the transmission cycle.

Human activity can lead to the movement of infected vectors, such as ticks on farmed animals and mosquitoes in trade goods, as well as the movement of infected hosts such as companion animals or livestock.

Not applicable.

Habitat change Habitat or environmental change can increase or decrease the suitability of an area for vector development and activity, affecting pathogen transmission. This is least important for midges

Habitat or environmental changes, including urbanisation, can affect the suitability for hosts and vectors, creating areas into which they can spread, and where they can get established and persist.

It is not relevant for midges.

Habitat change can be used to control vectors, for example modification of vegetation to control ticks or removal of larval breeding sites to control mosquitoes. It is not relevant for midges.

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Box 1. Examples of the importance of abundance and seasonality for each vector group

A. Tick abundance and Lyme disease

The complexity of the interplay between tick density, tick infection rate and human exposure is illustrated by the rural and urban risks of Lyme disease, transmitted by the sheep tick Ixodes ricinus. Tick densities tend to be higher in rural areas, where there are also more large mammalian hosts (deer, livestock). Infection is not always transmitted from these larger animals to the ticks, so this can cause a dilution effect creating lower tick infection rates. With the relatively low human populations densities in rural areas, contact rates between humans and ticks may also be low, so that even where infected tick density may be high, few humans will get infected and the risk of infection at a population level is low [12].

In urban areas, the habitats suitable for ticks may have relatively few large animal hosts, but they do support small mammalian and bird hosts which can transmit infection to ticks. As a consequence, even if tick densities are low; the infection rates may be higher because infection rates are not diluted by large mammal hosts. Furthermore, in towns, the high human population densities may mean that more people come into contact with ticks with higher infection rates. Therefore, the main drivers of disease are neither tick density nor high infection rate alone, but the density of infected ticks coupled with levels human exposure to the vectors.

B. Mosquito abundance and West Nile fever The severity of West Nile fever (WNF) outbreaks is determined by a complex interplay of avian host density, vector infection rate and human or animal exposure. For WNF, the numbers of human or horse cases are related to the numbers of bites by infected mosquitoes, and these numbers are themselves related to the intensity of pathogen transmission between birds and mosquitoes.

There is some limited field evidence to quantify the role of vector abundance in this system. During the 2012–

2015 WNF outbreaks in Serbia, there was a close match between the distribution of clusters of infected

mosquitoes, birds, horses and humans. There was also a strong positive correlation between both annual

maximum and average mosquito abundance, and infection rate with annual incidence of WNF cases [13].

In the 2010 WNF outbreak in Maricopa County, USA, densities of the mosquito vector Cx. quinquefasciatus were also higher in outbreak areas than regions with no cases [14,15].

Despite these correlations, the relationship between vector abundance and infection rates is not consistent.

The virus may be absent from areas of high mosquito density and WNF outbreaks may occur when vector abundance is low and the vector infection rate is high.

C. Sandfly abundance and leishmaniasis In both outbreak (Madrid) and endemic (Murcia, Catalonia) situations in Spain, high vector abundance has been associated with high infection rates of L. infantum in the sandfly vectors, in humans or in dogs [16–18]. The outbreak in Madrid clearly showed that high sandfly abundance could be a major factor triggering a human leishmaniosis epidemic. However, this outbreak was epidemiologically unusual as it was associated with an explosion in the population of lagomorph hosts in the green amenity areas that were integrated into new housing developments built on agricultural land. Sandfly infection rates in endemic areas, however, seem to vary greatly, ranging from 4% or less in Portugal [19–23] to 39%

in northwest Spain. None of these studies correlated sandfly abundance with L. infantum infection rate in vectors. Indeed there is evidence that high

prevalence of human leishmaniasis was associated with a relatively low density of infected sandflies [24].

In this group, the relationship between vector abundance and infection rates is also inconsistent and contradictory.

D. Midge abundance and BTV infection

The case of bluetongue infection in Europe illustrates the relationships between midge vector abundance, seasonality and pathogen establishment, transmission and spread. Over two decades, several serotypes of bluetongue virus (BTV) have emerged in the

Mediterranean Basin and (elsewhere) in Europe. Rise in temperature, due to climate change, is likely to have increased the abundance of the midge vector C. imicola populations in Spain, southern France and Italy during the 1990s, facilitating BTV emergence.

Rising temperatures have also led to changes in the biting rate and development rates of other midge species in north-western Europe making them more effective vectors [25]. After its introduction in 2006, BTV-8 was spread throughout northern Europe largely by wind-blown midge vectors [26]. The following year saw the disease spread from Belgium to the UK, Germany to Denmark and then to Sweden [27]. The speed of the BTV-8 spread in France was influenced by parameters linked to vector abundance, by land cover and by host density [28,29].

The interplay between abundance, seasonality and the other vector related factors provide the most insight into the drivers of epidemiological processes described by the concepts considered here. Table 2 shows the normalised scores summed for each vector related parameter by epidemiological concept and Table 3 shows the scores by vector group. The scoring in this table implies a fairly consistent pattern, as indicated by the distribution of the highest scores in red.

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Table 2. Sum of normalised scores by epidemiological concept Concept Vector

presence Vector

abundance Infection rate Behavioural

traits Seasonality Reservoir

host numbers Human

activity Habitat

change Sum

Early warning 8.0 8.6 6.8 7.4 8.3 7.4 7.4 6.7 60.6

Pathogen

establishment 7.7 10.1 10.3 10.1 7.6 8.9 7.0 8.0 69.7

Pathogen

persistence 5.6 8.6 8.4 7.6 7.6 9.9 6.4 8.0 62.1

Pathogen

transmission 7.4 10.8 10.9 10.9 8.3 9.2 8.9 8.9 75.3

Pathogen

spread 6.7 9.1 9.6 7.6 7.6 10.1 9.5 8.9 69.1

Pathogen

control 6.0 7.5 10.0 8.9 7.9 7.6 7.6 8.0 63.5

Total 41.3 54.7 55.9 52.5 47.2 53.1 46.8 48.5 400.0

Table 3. Sum of normalised scores by epidemiological concept and vector group Vector group Vector

presence Vector

abundance Infection rate Behavioural

traits Seasonality Reservoir

host numbers Human

activity Habitat

change Sum

Ticks 14.3 12.3 12.3 11.7 10.4 14.9 9.1 14.9 100

Mosquitoes 9.8 13.8 11.5 13.2 13.2 12.6 12.6 13.2 100

Midges 6.6 17.9 15.1 14.2 12.3 14.2 12.3 7.5 100

Sandflies 10.6 10.6 17.0 13.5 11.3 11.3 12.8 12.8 100

Total 41.3 54.7 55.9 52.5 47.2 53.1 46.8 48.5 400

Scores 9.0 or above in process and 14.0 or above in the vector group are shown in bold.

Abundance, vector infection rate, vector activity and behaviour, and host numbers are seen as the most important drivers of vector-borne disease epidemiology. By contrast, vector presence alone is seen as a relatively poor indicator (except for ticks), while the importance of seasonality and the effects of human activity and habitat change are considered to be of intermediate importance.

Abundance is to some degree important for all the epidemiological concepts, but most obviously in relation to establishment and transmission. Vector abundance matters most for midge- and mosquito-borne pathogens and somewhat less for those borne by ticks and sandflies. Seasonality alone is not generally seen as a frequent primary driver of any of the concepts. However, it is clear from Tables 2 and 3 that seasonality is considered a more important epidemiological driver for mosquitoes than the other groups and that it is an important determinant when combined with other drivers such as vector abundance. Vector season length is also not seen as a major driver of pathogen dynamics, except that it determines the vector-free period (when pathogen transmission by vectors is not possible).

These overarching patterns obviously conceal some characteristics of individual groups. Vector presence is most useful as an indicator of tick-borne disease, for which host abundance and the effect of the habitat on the vector are also key factors. This set of drivers highlights the difference between ticks and the other groups for which abundance, infection rate and vector behaviour are more widely identified as drivers of disease. The impact of anthropogenic influences on the vectors is considered to be comparatively small, especially for ticks.

Availability of abundance and seasonality data in the VectorNet database

It should be noted that much of the VectorNet database was compiled in the legacy of projects (TIGERMAPS, V- BORNE and VBORNET) and consists largely of presence/absence data for polygons, based on entomological observations, or on point data extracted from the literature or from national data surveillance programmes. While some of these data may have had numbers per trap or per sample, the earlier entries were converted to presence (or absence) per polygon or point location and recorded as such.

Collecting abundance data (numbers per sample) has only been a priority since 2016. An overview of the main methods used to sample abundance (and thus the potential for quantifying seasonality) of the four vector groups is provided in the Appendix. A summary of the data that have been assembled to date is provided below. While the field data in VectorNet were collected using standardised trapping techniques, and thus provide indicators of abundance per sample, most sites (with the exception of the Culicoides and the mosquito Culex pipiens) were sampled on a single date and were therefore not part of longitudinal studies. Therefore, they can only be used to provide indices of relative abundance within a group of sites sampled at the same time.

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VectorNet funded substantial amounts of field sampling activities from 2014 until 2018, which were largely executed using standardised sampling or trapping methods and which yielded data on vector numbers with known denominators (per trap, per drag). In addition, many of the results from the wide-ranging national surveillance programmes for Culicoides species for much of western Europe have been incorporated into the database. These longitudinal surveys conducted in response to the outbreaks of bluetongue and Schmallenberg virus in the first decade of the new millennium.

All four vector groups have been sampled in several hundred locations as illustrated in Figure 1. Ticks and mosquitoes have been sampled throughout the continent, with a focus on northern and eastern Europe for ticks, and central and eastern countries for mosquitoes. Sandfly sampling in peri-Mediterranean countries, the Balkans and the Caucasus was informed by spatial distribution models produced in preceding projects. The Culicoides data obtained are concentrated in western Europe, supplemented by significant numbers of field samples in the Balkans and a transect crossing eastern Europe from north to south. Extensive Culicoides data have also been collected for other countries [30], focusing on species complexes rather than individual species. In order to integrate new data from these sources, VectorNet is continually contacting national surveillance authorities.

Figure 1. Locations with abundance values derived from standardised sampling of active vectors, VectorNet database, as of March 2018

a) Midges

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b) Mosquitoes

c) Sandflies

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d) Ticks

Blue dots for ticks represent passive surveillance records.

For Culicoides midges, VectorNet holds rather extensive abundance data for much of Europe, and parts of northern Africa, though eastern European countries are less extensively sampled. These data therefore provide a reliable indicator of midge abundance in western Europe. For the other groups, few of the samples were longitudinal so there is little information on seasonality. There are, however, significant amounts of abundance data available for the four main tick species of interest, for some sandfly species, and for a few mosquito species (e.g. Aedes albopictus, Culex pipiens). For these groups, and for midges in the Balkans and some other areas, the

standardisation of sampling provides at least some indication of abundance for the majority of sampled locations, even if it is only for a single date and a limited set of neighbouring locations. This resource may offer an

opportunity to estimate abundance indices for limited regions or time periods, or perhaps provide opportunities for assessing the relationships between numbers and environmental drivers that could be used to generate models for larger areas. Additional information can be found in the section on spatial modelling of vector abundance and seasonality.

Requirements for abundance and seasonality assessments

The previous section demonstrates that the amount and coverage of vector abundance and seasonality data currently available for Europe and northern Africa are more complete for some groups than for others. With the exception of the Culicoides midges, longitudinal sampling efforts are available from relatively few locations at a continental scale, and both seasonality and harmonised abundance measures are comparatively rare. There remain, therefore, gaps in both the abundance and seasonality data for all groups. These gaps can be filled either by additional field sampling, or by model predictions that use environmental and existing distribution data to calculate consistent and complete coverage.

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Field sampling

Field sampling, especially the longitudinal sampling needed for abundance and seasonality estimates, is expensive.

Desk-based sampling methods are therefore likely to be the first port of call to acquire additional data. These methods include extraction from online databases such as EID2 [31] and systematic literature reviews. Though databases and literature reviews may provide useful information on abundance or seasonality, they are likely to focus on priority or high-profile species, and data for lower priority species tend to be excluded. Furthermore, neither method is guaranteed to provide information about particular areas of interest, and some form of further sampling or gap filling is likely to be necessary if continental level distribution maps are required.

Ways to prioritise which species and which locations should be sampled are set out in the Conclusions section below.

Overview of sampling strategies

A number of lessons have been learned during the VectorNet project. Sampling should be carried out using standardised or harmonised methods (which will be specific for each group) and run for several years to make sure that annual variation does not bias the outcomes. Transect sampling should be performed by standardised trapping starting at the beginning of season (potentially defined according to degree-days for each location) and continuing at regular two weekly intervals for the whole vector period that vectors are active. There should be sampling for consecutive weeks at 3–5 sites within each location.

Using large scale transects in Europe, S–N and E–W directed transects would reduce the amount of detailed sampling needed. If different trap types are used in different locations, conversion factors should be calculated from Latin square field comparisons or from evidence collated from the literature.

VectorNet experts from all vector groups were consistent in stating that it is not realistic to produce abundance (or seasonality) maps without substantially more resources than are needed for presence/absence maps with the same level of detail. Assuming, therefore, that the major aim of further field sampling is to fill the gaps in continental scale distributions (rather than, for example, to monitor the expansion of invasive species), sample or transect locations should be chosen to ‘fill the gaps’ and establish species distribution limits. If distribution models are available (see section on spatial modelling of vector abundance and seasonality), these can be used to determine where both the ‘edges’ and the major unsampled distribution foci are likely to be. Sample locations should also be well dispersed, rather than be close to regions for which data are available.

If, in addition to ‘filling the gaps’ in observed data, the objective of further sampling can be to feed spatial distribution models (see section on spatial modelling), then the target number of locations should be set

accordingly. The main issues that limit spatial distribution modelling is the number of sample points available and whether they are sufficiently dispersed to be representative of the entire area for which the modelled map is required. There are no hard and fast rules to define the sample number needed to produce a reliable map, as it depends on the desired level of uncertainty, the method, the values themselves, and the strength of the statistical relationships between the target variable and the covariates used, which depends on group, species, and life stage as well as on habitat type, structure and other factors. As a guideline, at least one sample point every 100 km (i.e.

10 000 km²) is required [32] for continental-scale areas, implying a minimum of 450 points for the EU.

There are a number of vector-group-specific issues that are summarised below.

Ticks

Earlier sections have suggested that ticks should be monitored by dragging of vegetation or estimating vector numbers on hosts. As for the other vector groups, sufficient longitudinal data will allow seasonal patterns in relative activity and relative abundance to be detected and can be compared with weather data on temperature and rainfall that can be robustly modelled (see section on spatial modelling below). A network of field stations where tick activity is monitored on a regular (preferably weekly) basis across the continent might be developed to generate data for a continent-wide temporal activity model. Data on tick seasonality and activity are likely to be more useful metrics to collect than vector abundance data, though these also require longitudinal sampling efforts.

Culicoides

Many extensive national schemes provide (or used to provide) seasonality and abundance estimates for Culicoides for much of western Europe. Annual maxima can be extracted from these data to compare abundance over time and locality, though additional information may be needed to establish seasonality as sampling dates may not have

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the project. Future data acquisition efforts should be focussed on obtaining these existing datasets generated by existing surveillance programmes.

Though these data could most probably be used to generate reliable models for the whole VectorNet region (EU/EEA and neighbouring countries), there is a case for field sampling along the eastern and southern margins of this region from which Culicoides data are not readily available. Sampling should follow the protocols used for the national programmes such as those in France, namely longitudinal samples from selected farms every two weeks throughout the season, with the peak annual number per trap used as the abundance metric.

For Culicoides, in addition to climate, livestock density and land cover have been demonstrated to be determinants of species occurrence as well as where and how many vectors can be found [33]. An efficient strategy to limit the number of collection sites for assessment of abundance would be to use latitudinal and altitudinal transects, while sampling different classes of livestock and land cover. The number of collection time points needed per year would be dependent on the length of the activity period. The number of collections necessary to estimate maximum abundance could be reduced in areas where the Culicoides dynamics has been previously characterised by targeted trapping.

Searle also states ‘Culicoides surveillance methods should be adapted to focus on concentrated assessments of species-specific abundance during the start and end of seasonal activity in temperate regions to facilitate refinement of ruminant movement restrictions thereby reducing the impact of Culicoides-borne arboviruses’ [33].

Searle also asserts that multi-species measures of diversity or richness are too variable to provide usable information [33].

Mosquitoes

Field work undertaken during VectorNet suggests a seasonal maximum of relative abundance might be the most suitable metric to describe continent-wide differences in basic ‘risk’ (Petric, personal communication) for pathogen transmission. Annual time series of relative abundance of eggs (e.g. Aedes albopictus) or adults (Culex pipiens complex) could be used to describe seasonality. Start and end date of vector activity (or vector-free period) can then be used to map the differences in seasonality across Europe.

A threshold level above which a vector’s abundance is considered to pose a risk or be a nuisance is likely to vary regionally, as well as with pathogen and vector species. The threshold abundance to indicate WNV transmission onset in Italy was set to 300 specimens of Cx. pipiens/trap/night [34], while the citizen science programmes within the same area declared 150 specimens of Cx. pipiens/trap/night as a nuisance threshold.

At least for C. pipiens, VectorNet field sampling showed that one measurement at one location can be

representative of a wider area and that a limited number of measurements per location at the beginning of the season gives a good indication of both seasonality and peak abundance later in the year. This has not been substantiated for other vector groups.

Sandflies

In rural areas, where sandflies are most common, they tend to concentrate where animals congregate, in farms and stables [35], but their abundance varies substantially with farm type [16]. Several sites of different type should therefore be sampled to get representative results. As with other species, standardised samples throughout the year can provide information on seasonality. As for mosquitoes, and indeed midges, annual maxima in numbers are likely to be a useful metric.

Light traps should be used and placed indoors in farms, close to walls and the floor, and within five meters of animal pens. Female sandflies are most abundant one to two meters from the animal group, and male sandflies may be more abundant four to five meters from the animal group [35]. Traps should be left for at least 24 hours.

In order to estimate seasonality, sampling should be repeated at least every two weeks. Shorter sampling intervals will increase precision of the estimated seasonality and of the relationship between abundance and changing weather conditions.

The importance of vector abundance and seasonality

TECHNICAL REPORT

The importance of

vector abundance and

seasonality

ECDC TECHNICAL REPORT

The importance of vector abundance and seasonality

Results from an expert consultation

Contents

Figures

Tables

Abbreviations

Glossary

Executive summary

Background

Types of measures of abundance and seasonality

Using abundance and seasonality data

Methods

Vector-related parameters

Epidemiological concepts

Results and discussion

Assessment of the importance of abundance and seasonality

Box 1. Examples of the importance of abundance and seasonality for each vector group

Availability of abundance and seasonality data in the VectorNet database

Requirements for abundance and seasonality assessments

Field sampling

Overview of sampling strategies

Ticks

Culicoides

Mosquitoes

Sandflies